To perform mixup, we need to collect a pool of phone-level features and their corresponding GOP scores. Specifically, an acoustic model, trained with paired speech signal and transcription, is utilized for deep features and GOPs score extraction. As shown in Figure REF  (a), given a collection of non-native audio with canonical transcription, we first extract MFCC features from the speech signal. An acoustic model is utilized to force-align all non-native audio (pairs of MFCC features and canonical transcription) to get the start and end time boundary for each phoneme. The MFCCs of each phoneme are then fed into the acoustic model to extract corresponding deep features and frame-level posterior probability from the bottleneck layer [1]} and output layer, respectively. Substantially, the GOP score of a specific phoneme can be interpreted as the normalized posterior probability over the phoneme duration obtained by the force-alignment.
Finally, a pool of phone-level features and corresponding GOP scores is collected for each phoneme.
